What Drives People’s Willingness to Adopt Autonomous Vehicles? A Review of Internal and External Factors

Abstract

This article presents a state-of-the-art literature review to understand people’s perceptions and opinions of Autonomous Vehicles and the factors that influence their adoption. A strategic literature search was conducted to select articles for this review. Most of the articles were published since 2015 and they used a household questionnaire survey to collect data. Mostly, they used statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt Autonomous Vehicles. The results show that psychological factors often appear as the most important internal factors of people’s willingness to adopt Autonomous Vehicles. Additionally, other internal factors such as the socioeconomic profile of individuals and their household, and knowledge and familiarity with Autonomous Vehicle technologies would affect adoption tendencies. User attributes also indirectly affect adoption of Autonomous Vehicles by influencing the psychological factors of users. We identify several critical external factors such as opportunities (e.g., safety and security, low congestion, energy use) and challenges (e.g., system failures, privacy breaches, and legal issues), while another influential group includes transportation factors (e.g., travel mode, distance, and time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the institutional regulatory environment. We discuss some recommendations for policy makers, auto industries, and private stakeholders to formulate policies and strategies to increase the market share of Autonomous Vehicles. Finally, we identify some limitations of previous studies and provide a blueprint for future research on Autonomous Vehicle adoption.

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DFA-SAT: Dynamic Feature Abstraction with Self-Attention-Based 3D Object Detection for Autonomous Driving

“…We have made a fair comparison of our proposed method with other existing methods in terms of hardware configuration variations and other parameter variations like speed and memory. We feed a similar quantity (16,384) of input point clouds and OpenPCDet [68] configuration is followed for memory. Our method also shows average results in GPU memory consumption reports, as shown in Table 6.…”

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Gauging Public Acceptance of Conditionally Automated Vehicles in the United States

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